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      "provenance": [],
      "include_colab_link": true
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/VyacheslavGusev/Machine_Lerning/blob/main/ML_Lineal_regression.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RReD2acL9zx6"
      },
      "source": [
        "import pandas as pd"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "q8Gcz4yH_dwq"
      },
      "source": [
        "# Загружаем данные в переменную df из файла homework.csv"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0y7mi8Hd951i"
      },
      "source": [
        "df = pd.read_csv(\"homework.csv\")"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 363
        },
        "id": "RUHLnmW_-BjK",
        "outputId": "462e2476-a9dc-49fc-c060-47f7ec99064b"
      },
      "source": [
        "df.head(10)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   ID     crim    zn  indus  chas    nox     rm   age     dis  rad  tax  \\\n",
              "0   1  0.00632  18.0   2.31     0  0.538  6.575  65.2  4.0900    1  296   \n",
              "1   2  0.02731   0.0   7.07     0  0.469  6.421  78.9  4.9671    2  242   \n",
              "2   4  0.03237   0.0   2.18     0  0.458  6.998  45.8  6.0622    3  222   \n",
              "3   5  0.06905   0.0   2.18     0  0.458  7.147  54.2  6.0622    3  222   \n",
              "4   7  0.08829  12.5   7.87     0  0.524  6.012  66.6  5.5605    5  311   \n",
              "5  11  0.22489  12.5   7.87     0  0.524  6.377  94.3  6.3467    5  311   \n",
              "6  12  0.11747  12.5   7.87     0  0.524  6.009  82.9  6.2267    5  311   \n",
              "7  13  0.09378  12.5   7.87     0  0.524  5.889  39.0  5.4509    5  311   \n",
              "8  14  0.62976   0.0   8.14     0  0.538  5.949  61.8  4.7075    4  307   \n",
              "9  15  0.63796   0.0   8.14     0  0.538  6.096  84.5  4.4619    4  307   \n",
              "\n",
              "   ptratio   black  lstat  medv  \n",
              "0     15.3  396.90   4.98  24.0  \n",
              "1     17.8  396.90   9.14  21.6  \n",
              "2     18.7  394.63   2.94  33.4  \n",
              "3     18.7  396.90   5.33  36.2  \n",
              "4     15.2  395.60  12.43  22.9  \n",
              "5     15.2  392.52  20.45  15.0  \n",
              "6     15.2  396.90  13.27  18.9  \n",
              "7     15.2  390.50  15.71  21.7  \n",
              "8     21.0  396.90   8.26  20.4  \n",
              "9     21.0  380.02  10.26  18.2  "
            ],
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              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>ID</th>\n",
              "      <th>crim</th>\n",
              "      <th>zn</th>\n",
              "      <th>indus</th>\n",
              "      <th>chas</th>\n",
              "      <th>nox</th>\n",
              "      <th>rm</th>\n",
              "      <th>age</th>\n",
              "      <th>dis</th>\n",
              "      <th>rad</th>\n",
              "      <th>tax</th>\n",
              "      <th>ptratio</th>\n",
              "      <th>black</th>\n",
              "      <th>lstat</th>\n",
              "      <th>medv</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
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              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>0.00632</td>\n",
              "      <td>18.0</td>\n",
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              "      <th>1</th>\n",
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              "      <td>4.9671</td>\n",
              "      <td>2</td>\n",
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              "      <td>17.8</td>\n",
              "      <td>396.90</td>\n",
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              "      <td>21.6</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>4</td>\n",
              "      <td>0.03237</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2.18</td>\n",
              "      <td>0</td>\n",
              "      <td>0.458</td>\n",
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              "      <td>18.7</td>\n",
              "      <td>394.63</td>\n",
              "      <td>2.94</td>\n",
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              "      <th>3</th>\n",
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              "      <td>54.2</td>\n",
              "      <td>6.0622</td>\n",
              "      <td>3</td>\n",
              "      <td>222</td>\n",
              "      <td>18.7</td>\n",
              "      <td>396.90</td>\n",
              "      <td>5.33</td>\n",
              "      <td>36.2</td>\n",
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              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>7</td>\n",
              "      <td>0.08829</td>\n",
              "      <td>12.5</td>\n",
              "      <td>7.87</td>\n",
              "      <td>0</td>\n",
              "      <td>0.524</td>\n",
              "      <td>6.012</td>\n",
              "      <td>66.6</td>\n",
              "      <td>5.5605</td>\n",
              "      <td>5</td>\n",
              "      <td>311</td>\n",
              "      <td>15.2</td>\n",
              "      <td>395.60</td>\n",
              "      <td>12.43</td>\n",
              "      <td>22.9</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>11</td>\n",
              "      <td>0.22489</td>\n",
              "      <td>12.5</td>\n",
              "      <td>7.87</td>\n",
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              "      <td>0.524</td>\n",
              "      <td>6.377</td>\n",
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              "      <td>5</td>\n",
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              "      <td>15.2</td>\n",
              "      <td>392.52</td>\n",
              "      <td>20.45</td>\n",
              "      <td>15.0</td>\n",
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              "      <th>6</th>\n",
              "      <td>12</td>\n",
              "      <td>0.11747</td>\n",
              "      <td>12.5</td>\n",
              "      <td>7.87</td>\n",
              "      <td>0</td>\n",
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              "      <td>6.009</td>\n",
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              "      <td>13.27</td>\n",
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              "      <th>7</th>\n",
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              "      <td>15.2</td>\n",
              "      <td>390.50</td>\n",
              "      <td>15.71</td>\n",
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            ]
          },
          "metadata": {},
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1ALk34jIU9gW",
        "outputId": "562f4766-89d4-4ba1-bac5-53de6c905a8d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(333, 15)"
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TC5PJrHC-C3U"
      },
      "source": [
        "import matplotlib.pyplot as plt"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RdrTsIP8_kEq"
      },
      "source": [
        "# Построить scatter график по параметрам crim, medv"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 447
        },
        "id": "G2w28i06-Jk0",
        "outputId": "278ac5ef-b45c-4e05-cdd5-bb6db890478c"
      },
      "source": [
        "plt.scatter(\n",
        "    df['crim'],\n",
        "    df['medv']\n",
        ")"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.collections.PathCollection at 0x7fa63fd62590>"
            ]
          },
          "metadata": {},
          "execution_count": 5
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mj8AbZdq_svI"
      },
      "source": [
        "# Построить scatter график по параметрам rm, medv"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 447
        },
        "id": "jmDLcMaI-XTw",
        "outputId": "3243fed9-6df3-4357-9d96-7c19dd9659f8"
      },
      "source": [
        "plt.scatter(\n",
        "    df['rm'],\n",
        "    df['medv']\n",
        ")"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.collections.PathCollection at 0x7fa63fc738e0>"
            ]
          },
          "metadata": {},
          "execution_count": 6
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6FMjJQDi_z23"
      },
      "source": [
        "# Сформировать X и y из df, выбрав нужные колонки"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Fcel7fwcYCOx",
        "outputId": "8919191f-4fe7-43d0-bf9b-38a624f7ad96"
      },
      "source": [
        "df.columns"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['ID', 'crim', 'zn', 'indus', 'chas', 'nox', 'rm', 'age', 'dis', 'rad',\n",
              "       'tax', 'ptratio', 'black', 'lstat', 'medv'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JJHBVVS4-2i3"
      },
      "source": [
        "X, y = df[['ID', 'crim', 'zn', 'indus', 'chas', 'nox', 'rm', 'age', 'dis', 'rad',\n",
        "       'tax', 'ptratio', 'black', 'lstat']], df['medv']"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "zCbCmq5PUhPx",
        "outputId": "3e35de2b-7ec9-4261-c943-43f397608164"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   ID     crim    zn  indus  chas    nox     rm   age     dis  rad  tax  \\\n",
              "0   1  0.00632  18.0   2.31     0  0.538  6.575  65.2  4.0900    1  296   \n",
              "1   2  0.02731   0.0   7.07     0  0.469  6.421  78.9  4.9671    2  242   \n",
              "2   4  0.03237   0.0   2.18     0  0.458  6.998  45.8  6.0622    3  222   \n",
              "3   5  0.06905   0.0   2.18     0  0.458  7.147  54.2  6.0622    3  222   \n",
              "4   7  0.08829  12.5   7.87     0  0.524  6.012  66.6  5.5605    5  311   \n",
              "\n",
              "   ptratio   black  lstat  \n",
              "0     15.3  396.90   4.98  \n",
              "1     17.8  396.90   9.14  \n",
              "2     18.7  394.63   2.94  \n",
              "3     18.7  396.90   5.33  \n",
              "4     15.2  395.60  12.43  "
            ],
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              "      <div>\n",
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              "      <th>ID</th>\n",
              "      <th>crim</th>\n",
              "      <th>zn</th>\n",
              "      <th>indus</th>\n",
              "      <th>chas</th>\n",
              "      <th>nox</th>\n",
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              "      <th>dis</th>\n",
              "      <th>rad</th>\n",
              "      <th>tax</th>\n",
              "      <th>ptratio</th>\n",
              "      <th>black</th>\n",
              "      <th>lstat</th>\n",
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              "      <td>396.90</td>\n",
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              "      <th>2</th>\n",
              "      <td>4</td>\n",
              "      <td>0.03237</td>\n",
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              "      <td>2.18</td>\n",
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              "      <td>3</td>\n",
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              "      <td>18.7</td>\n",
              "      <td>394.63</td>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>5</td>\n",
              "      <td>0.06905</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2.18</td>\n",
              "      <td>0</td>\n",
              "      <td>0.458</td>\n",
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              "      <td>18.7</td>\n",
              "      <td>396.90</td>\n",
              "      <td>5.33</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>7</td>\n",
              "      <td>0.08829</td>\n",
              "      <td>12.5</td>\n",
              "      <td>7.87</td>\n",
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              "      <td>0.524</td>\n",
              "      <td>6.012</td>\n",
              "      <td>66.6</td>\n",
              "      <td>5.5605</td>\n",
              "      <td>5</td>\n",
              "      <td>311</td>\n",
              "      <td>15.2</td>\n",
              "      <td>395.60</td>\n",
              "      <td>12.43</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-c1c7dca7-609a-490c-b594-9212a0a8a4c8')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
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              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
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              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
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              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-c1c7dca7-609a-490c-b594-9212a0a8a4c8 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-c1c7dca7-609a-490c-b594-9212a0a8a4c8');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
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              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xxFRNeGUUo9A",
        "outputId": "c7b43171-0fce-40ac-9939-fbc2dd18e6e4"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    24.0\n",
              "1    21.6\n",
              "2    33.4\n",
              "3    36.2\n",
              "4    22.9\n",
              "Name: medv, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "i7zKm0JQ-mvB"
      },
      "source": [
        "from sklearn.model_selection import train_test_split"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "r5O1-Mzjjmeb"
      },
      "source": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9UUFvp5xAH44"
      },
      "source": [
        "# Разбить данные на данные для обучения и проверки"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Ot5eY2wMAK8j"
      },
      "source": [
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "acpQ0NA9-bP-"
      },
      "source": [
        "from sklearn.linear_model import LinearRegression"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TAWGJsLDANIl"
      },
      "source": [
        "# Создать и обучить модель LinearRegression"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "w2AUaX0tAQ2v"
      },
      "source": [
        "model = LinearRegression()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "H75h4xr7Z7lY",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        },
        "outputId": "ab0913f8-db66-4cd3-898f-5323711355fc"
      },
      "source": [
        "model.fit(X_train, y_train)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LinearRegression()"
            ],
            "text/html": [
              "<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LinearRegression()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LinearRegression</label><div class=\"sk-toggleable__content\"><pre>LinearRegression()</pre></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model.coef_"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "LIR1gdPvXlin",
        "outputId": "75ce03eb-6374-400a-a019-e77f7af0d513"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([-5.47277839e-03, -2.85052948e-02,  3.17547265e-02,  2.30199462e-02,\n",
              "        2.80046077e+00, -1.13464204e+01,  4.09406740e+00, -1.27983053e-02,\n",
              "       -1.45991545e+00,  4.36815830e-01, -1.54474602e-02, -8.93892662e-01,\n",
              "        1.09471484e-02, -6.48741279e-01])"
            ]
          },
          "metadata": {},
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XYDaz_91_Knm"
      },
      "source": [
        "from sklearn.metrics import mean_squared_error"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VHYNZW64ASnM"
      },
      "source": [
        "# Оценить качество на тестовой выборки"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PQb8yYkFrIzu",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "89b51af8-7d6e-4870-82b1-22c4bb318ab9"
      },
      "source": [
        "mean_squared_error(y_test, model.predict(X_test))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "22.627710990509975"
            ]
          },
          "metadata": {},
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "mean_squared_error(y_train, model.predict(X_train))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0A1X1VVjYP_q",
        "outputId": "e8d0575b-f10a-400c-f0b7-520150c8dcd2"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
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          "metadata": {},
          "execution_count": 23
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}